Week 1 HW: Principles and Practices

This page includes Class Assigment and Week 2 Lecture Preparation Questions

Class Assignment

1. First, describe a biological engineering application or tool you want to develop and why. This could be inspired by an idea for your HTGAA class project and/or something for which you are already doing in your research, or something you are just curious about.

For HTGAA 2026, I’d like to propose the design and development of a synthetic biology based microbial system for the improvement of agricultural productivity in saline soils of the Bolivian Altiplano. This is because oil salinization is continuing to progress in the high-altitude areas of Bolivia as a consequence of climate change, water shortage and historical land use (Andrade, 2025). According to the Food and Agriculture Organization (n.d.), already a considerable fraction of irrigated and arid agricultural lands worldwide face the challenge of soil salinity. Scientific studies have shown that soil salinity significantly reduces crop yields, alters soil biological functions, and directly threatens food security, particularly in smallholder farming systems (Farooq et al., 2021). In the same way, the majority of smallholder farmers in the Altiplano rely on marginal soils, often where conventional fertilizers cannot be used effectively or are economically unaffordable and are a direct threat to local food security and livelihoods from salinization. This is why my proposed project aims to investigate the conceptual design for soil microorganisms that can sense such high salinity and improve soil structure and plant stress tolerance. However, beyond its technical feasibility, this application raises relevant ethical, environmental and governance issues surrounding environmental release and biosafety and also equitable access to biotechnology. Finally, as a Bolivian, I see this work as an opportunity to link cutting edge biological engineering with locally anchored solutions that address real challenges faced by vulnerable agricultural communities in my country.

2. Next, describe one or more governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm). Break big goals down into two or more specific sub-goals. Below is one example framework (developed in the context of synthetic genomics) you can choose to use or adapt, or you can develop your own. The example was developed to consider policy goals of ensuring safety and security, alongside other goals, like promoting constructive uses, but you could propose other goals for example, those relating to equity or autonomy.

Main Goal: Ensuring Environmental Safety and Biosecurity This goal focuses on preventing ecological harm and unintended consequences associated with the environmental use of engineered microorganisms.

• Sub-goal 1: Preventing Uncontrolled Spread

-> Design biological containment mechanisms to limit survival outside target environments.

-> Require environmental risk assessments prior to any field deployment.

• Sub-goal 2: Reducing Ecological Uncertainty

-> Promote long-term monitoring of soil and microbial ecosystem impacts.

-> Establish protocols for detecting and responding to unintended ecological effects.

Main Goal: Promoting Equity and Responsible Use This goal ensures that the benefits of the technology reach vulnerable communities without reinforcing existing inequalities.

• Sub-goal 1: Supporting Smallholder Farmers

-> Ensure that the technology is affordable and adapted to local agricultural contexts.

-> Encourage community involvement in deployment decisions.

• Sub-goal 2: Preventing Technological Exploitation

-> Avoid extractive research practices in developing regions.

-> Promote benefit-sharing and local capacity building.

3. Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”). Try to outline a mix of actions (e.g. a new requirement/rule, incentive, or technical strategy) pursued by different “actors” (e.g. academic researchers, companies, federal regulators, law enforcement, etc). Draw upon your existing knowledge and a little additional digging, and feel free to use analogies to other domains (e.g. 3D printing, drones, financial systems, etc.). Purpose: What is done now and what changes are you proposing? Design: What is needed to make it “work”? (including the actor(s) involved - who must opt-in, fund, approve, or implement, etc) Assumptions: What could you have wrong (incorrect assumptions, uncertainties)? Risks of Failure & “Success”: How might this fail, including any unintended consequences of the “success” of your proposed actions?

A) Biosafety by design through genetic containment. Purpose: Current agricultural biotechnology often relies on external monitoring after deployment. This action proposes embedding biosafety mechanisms directly into the engineered organisms.

Design:

• Implemented by academic researchers and biotech developers.
• Reviewed by institutional biosafety committees and environmental regulators.

Assumptions: Genetic containment systems function reliably in complex soil environments.

Risks of Failure & “Success”:

• Failure: Evolutionary escape from containment mechanisms
• Unintended Success: Reduced emphasis on ecological monitoring due to overconfidence in technical controls.

B) Regulatory frameworks for environmental synthetic biology. Purpose: Environmental release regulations are often unclear or inconsistent. This action proposes clearer regulatory pathways specific to environmental synthetic biology.

Design: National environmental and agricultural agencies conduct standardized risk assessments.

Assumptions: Regulators have sufficient technical expertise.

Risks of Failure & “Success”:

• Failure: Overregulation slows innovation
• Unintended Success: Rapid approval without sufficient local adaptation

C) Community centered deployment and oversight. Purpose: Agricultural technologies should align with the needs and values of affected communities.

Design:

• Collaboration among researchers, NGOs, and local farming communities.
• Participatory decision making processes.

Assumptions: Community participation is meaningful and informed.

Risks of Failure & “Success”:

• Failure: Delays due to conflicting priorities.
• Unintended Success: Token participation without real influence.

4. Next, score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. The following is one framework but feel free to make your own:

5. Next, score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. The following is one framework but feel free to make your own:

Based on the comparative scoring of the governance options, the approach that I would prioritize is a combination of biosafety by design and community centered governance. This is because embedding safety mechanisms directly into engineered soil microorganisms is essential to prevent unintended ecological harm and to address biosecurity concerns at the earliest stage of development. This option performs strongly in preventing incidents and maintaining laboratory and environmental safety, making it a foundational requirement for any responsible application of environmental synthetic biology. At the same time, community centered governance is critical for ensuring that this technology is ethically deployed in the Bolivian Altiplano and engaging local farming communities helps align the technology with real agricultural needs, promotes trust and reduces the risk of inequitable or extractive use.

Reflecting on what you learned and did in class this week, outline any ethical concerns that arose, especially any that were new to you. Then propose any governance actions you think might be appropriate to address those issues. This should be included on your class page for this week.

A key ethical concern that stood out to me was the increasing use of artificial intelligence in synthetic biology because AI tools can greatly accelerate the design of engineered microorganisms, such as those proposed in my project to improve agricultural productivity in saline soils of the Bolivian Altiplano. However, a new ethical issue for me was the possibility that decisions driven by AI models may lack transparency or embed biases, potentially leading to unintended ecological consequences when organisms are applied in open environments. In consequence, to address these issues, I would suggest appropriate governance actions; for example, transparency in the use of AI for biological design, rigorous validation and risk assessment prior to environmental application. In addition, governance frameworks should encourage participatory approaches that involve local communities and ensure that resulting technologies are accessible, safe and aligned with local agricultural needs.

Assignment (Week 2 Lecture Prep)

Homework Questions from Professor Jacobson:

1. Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome? How does biology deal with that discrepancy?

DNA polymerase copies DNA with high accuracy as the raw error rate of DNA polymerase is about 1 mistake per 10⁵ nucleotides copied. However, most DNA polymerases also have a proofreading function which corrects many of these mistakes, improving accuracy to about 1 error per 10⁷ - 10⁸ nucleotides and after replication, additional DNA repair systems fix remaining errors, bringing the final error rate to roughly 1 mistake per 10⁹ - 10¹⁰ nucleotides. On the other hand, the human genome is about 3 × 10⁹ base pairs long which means that without repair, thousands of errors would occur when a cell divides. For the last question, biology deals with this discrepancy through three layers of control which are polymerase proofreading, mismatch repair and other DNA repair pathways, keeping mutation rates low enough for genome stability while still allowing evolution.

2. How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?

Proteins are encoded using codons which are groups of three DNA nucleotides and there are 64 possible codons but only 20 amino acids plus stop signals. It is for this reason that most amino acids are encoded by multiple codons being this called degeneracy of the genetic code. On the other side, for an average human protein of about 400 amino acids, the number of possible DNA sequences that could encode the same protein is more than 10¹⁹ possible sequences. However, in practice, most of these sequences do not work well because some codons are translated more efficiently, certain sequences affect mRNA stability and others create unwanted secondary structures meanwhile some interfere with translation speed and protein folding. Moreover, regulatory elements, splicing signals, and GC content also limit which DNA sequences can successfully produce a functional protein in real cells.

Homework Questions from Dr. LeProust:

1.What’s the most commonly used method for oligo synthesis currently?

The most widely used nowadays is solid - phase phosphoramidite chemical synthesis and in this method DNA is built one nucleotide at a time on a solid support (controlled - pore glass). Also, each synthesis cycle adds one base through chemical reactions (deprotection, coupling, capping, oxidation) making this process fast and reliable for short DNA sequences being this the reason why it dominates both research and commercial oligo production.

2. Why is it difficult to make oligos longer than 200nt via direct synthesis?

Because each synthesis step is not 100% efficient. As oligos get longer, small inefficiencies compound leading to incorrect sequences. For example, after 200 cycles the fraction of full - length, correct molecules drops sharply. In addition, longer oligos accumulate chemical side products, have higher error rates and are harder to purify.

3. Why can’t you make a 2000bp gene via direct oligo synthesis? Because a 2000 bp gene would require 2000 consecutive chemical synthesis cycles that would result in a low yield of correct full - length DNA due to errors and the final product would be dominated by short fragments and mutated sequences, making purification not practical. Instead, long genes are made by assembling shorter, high - quality oligos through Gibson assembly or Golden Gate which improves accuracy and yield.

Homework Question from George Church:

1. [Using Google & Prof. Church’s slide #4] What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

The 10 essential amino acids that all animals have are histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, and arginine (HyperPhysics, n.d.). On the other hand my view of “lysine contingency” now makes me think that as all animals require lysine from their environment, synthetic biology could turn this constraint into a design principle in this area by engineering organisms that depend on externally supplied lysine and scientists would be able to control growth, improve biosafety and limit ecological spread. This would be very interesting for applications in agriculture, in my opinion.

References.

Andrade, D. (2025). Characterization, prediction, and remediation of salt-affected soils in the High Valley of Cochabamba - Bolivia (Doctoral thesis, Université de Liège - Gembloux Agro-Bio Tech). ORBi-University of Liège. https://orbi.uliege.be/handle/2268/325556

Farooq, M., et al. (2021). Climate change and salinity effects on crops and plant–microbe interactions. Frontiers in Sustainable Food Systems, 5, 618092. https://www.frontiersin.org/articles/10.3389/fsufs.2021.618092/full

Food and Agriculture Organization of the United Nations. (n.d.). Soil salinity. FAO Global Soil Partnership. https://www.fao.org/global-soil-partnership/areas-of-work/soil-salinity/en/

HyperPhysics. (n.d.). Essential Amino Acids. HSC.edu.kw. http://www.hsc.edu.kw/student/materials/Physics/website/hyperphysics%20modified/hbase/organic/essam.html